pmrf.fitting.fitters.ScipyMinimizeFitter

class pmrf.fitting.fitters.ScipyMinimizeFitter(model, measured, frequency=None, features=None, cost=None, *args, **kwargs)

Bases: FrequentistFitter

Scipy fitter using scipy.minimize.

Initializes the FrequentistFitter.

Args:

model (Model):

The parametric pmrf model to be fitted.

measured (skrf.Network | list[skrf.Network]):

The measured network data to fit the model against.

frequency (skrf.Frequency | None, optional):

The frequency axis to perform the fit on. Defaults to None.

features (FeatureT | FeatureListT | None, optional),

The features to extract for comparison. Defaults to None.

cost (ArrayFuncT | list[ArrayFuncT] | eqx.Module, optional):

A function or sequence of functions defining the cost metric. If a list of functions is provided, they are composed sequentially. If None, a default cost function (typically L2 norm on the dB magnitude difference) is used. Defaults to None.

Parameters:

• model (Model)

• measured (Network | dict[str, Network])

• frequency (Frequency | None)

• features (str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]] | dict[str, str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]]] | None)

• cost (Callable[[Array], Array] | list[Callable[[Array], Array]] | Module)

__init__(model, measured, frequency=None, features=None, cost=None, *args, **kwargs)

Initializes the FrequentistFitter.

Args:

model (Model):

The parametric pmrf model to be fitted.

measured (skrf.Network | list[skrf.Network]):

The measured network data to fit the model against.

frequency (skrf.Frequency | None, optional):

The frequency axis to perform the fit on. Defaults to None.

features (FeatureT | FeatureListT | None, optional),

The features to extract for comparison. Defaults to None.

cost (ArrayFuncT | list[ArrayFuncT] | eqx.Module, optional):

A function or sequence of functions defining the cost metric. If a list of functions is provided, they are composed sequentially. If None, a default cost function (typically L2 norm on the dB magnitude difference) is used. Defaults to None.

Parameters:

• model (Model)

• measured (Network | dict[str, Network])

• frequency (Frequency | None)

• features (str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]] | dict[str, str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]]] | None)

• cost (Callable[[Array], Array] | list[Callable[[Array], Array]] | Module)

Return type:

None

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(model, measured[, frequency, ...])	Initializes the FrequentistFitter.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(*args, **kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_bounds()
_make_cost_function([as_numpy])
_make_feature_function([as_numpy])
_settings([solver_kwargs, fitter_kwargs])
run(**kwargs)	Executes the fitting algorithm.

Attributes

__abstractmethods__
__annotations__
__dict__
__doc__
__module__
__slots__
__weakref__	list of weak references to the object
_abc_impl

run(**kwargs)

Executes the fitting algorithm.

This method must be implemented by all concrete subclasses. It is the main entry point to start the optimization or sampling process.

Returns:

FitResults: An object containing the results of the fit.

Return type:

FrequentistResults

initial_model: Model

measured: skrf.Network | dict[str, skrf.Network]